As is known, Internet and the Web have become the key enablers which have motivated and rendered possible the revolution in the management of all the steps necessary for the use of images in digital format, i.e., the so-called “imaging workflow.” This emerging workflow structure depends upon the effective implementation of three fundamentals steps: image acquisition, the so-called “digital way-in;” image re-utilization, the so-called “digital recirculation;” and cross-device image rendering, the so-called “digital way-out,” i.e., the rendering of the images among heterogeneous devices (monitor, printer, etc.), in particular, the processing of the images for a specific purpose, such as printing or filing.
A content-based digital-image classification has by now become an indispensable need for an accurate description and use of digital images, particularly for the adoption of the most suitable image-processing strategies for satisfying the ever-increasing demand for quality of image, speed of transmission, and ease of use in Internet-based applications, such as improvement of digital images, i.e., the so-called “image enhancement,” color-processing, and image compression.
At present, one of the methodologies used for content-based digital-image classification is essentially based on an approach of a heuristic type, implemented by means of expert systems. In other words, this methodology basically involves determination of the content of the image by analyzing the digital image in regions of variable size according to directions and pre-set scanning rules using an algorithm of the type “if . . . then . . . else,” i.e., by evaluating the meaning of the region of interest in the light of the characteristics of the preceding or adjacent regions, as well as by the verification of a structured sequence of membership conditions with one or more rules.
Although widely used, the above methodology presents a number of drawbacks. The first drawback is represented by the computational complexity required for analysis of the high number of pixels of an image, along with the other evident drawbacks in terms of time and cost associated thereto. The second drawback is represented by the extremely complex optimization that this methodology may be subject to. The third drawback is represented by the substantial impossibility of optimizing analysis using parallel architectures. The fourth drawback is due to the not extremely high intrinsic “robustness” of the methodology, caused by the unavoidable possibility of not considering, in the above-mentioned “if . . . then . . . else” algorithm, particular cases that may arise in images.